1. Field of the Invention
This invention generally relates to the field of image processing and, more particularly, to a system and method for processing and classifying biometric images, especially fingerprint images.
2. Background Description
There exist systems for accomplishing automatic authentication or identification of a person using a biometric measurement, that is, a measurement of a behavioral or physical characteristic of that person. One such physical characteristic is a person's fingerprint. A fingerprint of a person comprises a distinctive and unique ridge pattern structure which allows for such identification of a single person among millions of others.
The fingerprints that are observed may be grouped into sets of similar-looking prints, which are termed classes. Fingerprint classification is the process of assigning a fingerprint to one of a number of these predetermined classes. Fingerprint classification has been practiced for many years as a method of indexing large databases of fingerprints. Such class-based indexing allows quicker location of a particular individual's prints. If a print is known to be of class X, then only those database fingerprints of class X need be examined to look for a match.
FIGS. 1A to 1E show five different fingerprints, each having distinctive characteristics. These fingerprints are classified in different classes of the Henry classification system based on their overall ridge patterns. FIGS. 1A and 1B show left and right loop patterns, respectively.
FIGS. 1C and 1D show tented arch and arch patterns, respectively. FIG. 1E shows a whorl pattern. The system of classification used by human experts at the Federal Bureau of Investigations (FBI) has been described in The Science of Fingerprints (Classification and Uses), Superintendent of Documents, U.S. Government Printing office, Washington D.C. 20402: US Department of Justice, 1284 edition, (1984).
Recently, attempts have been made to automatically classify fingerprints using computers. This allows the more rapid indexing of fingerprint databases and can improve the speed and accuracy of Automatic Fingerprint Identification Systems (AFISs) which attempt the automatic identification of an individual based on the individual's fingerprint.
Previous systems have not achieved an adequate accuracy. Performance figures have been quoted in the pattern recognition literature for a number of fingerprint classification systems. In no case have the error rates been sufficiently low to justify using the technique in an AFIS.
Previous systems have assumed the use of rolled fingerprints to be necessary, whereas many automatic fingerprint identification systems are now designed to use dabs, where the fingerprint is only of the area of the finger in contact with a flat surface at an instant of time. In particular, live scan dabs are used. In a live scan dab, the print is captured by an electronic device incorporating optical, electronic or ultrasound sensors.
Rolled prints have a greater area, and therefore provide more information as to the fingerprint class. In particular, many previous fingerprint classification methods are rule-based, relying on the detection of the core and delta features of a fingerprint which may not necessarily be available in a dab fingerprint.